Towards Causal Effect Estimation of Emotional Labeling of Watched Videos

E. Pereira, Geovane do Nascimento Silva
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引用次数: 0

Abstract

Emotions play a crucial role in human life, they are measured using many approaches. There are also many methodologies for emotion elicitation. Emotion elicitation through video watching is one important approach used to create emotion datasets. However, the causation link between video content and elicited emotions was not well explained by scientific research. In this article, we present an approach for computing the causal effect of video content on elicited emotion. The Do-Calculus theory was employed for computing causal inference, and a SCM (Structured Causal Model) was proposed considering the following variables: EEG signal, age, gender, video content, like/dislike, and emotional quadrant. To evaluate the approach, EEG data were collected from volunteers watching a sample of videos from the LIRIS-ACCEDE dataset. A total of 48 causal effects was statistically evaluated in order to check the causal impact of age, gender, and video content on liking and emotion. The results show that the approach can be generalized for any dataset that contains the variables of the proposed SCM. Furthermore, the proposed approach can be applied to any other similar dataset if an appropriate SCM is provided.
观看视频情感标签的因果效应估计
情绪在人类生活中扮演着至关重要的角色,人们可以用很多方法来衡量情绪。情感激发也有很多方法。通过观看视频来激发情感是创建情感数据集的一种重要方法。然而,视频内容与引发情绪之间的因果关系并没有得到科学研究的很好解释。在本文中,我们提出了一种计算视频内容对诱发情绪的因果效应的方法。采用Do-Calculus理论进行因果推理,并考虑脑电信号、年龄、性别、视频内容、喜欢/不喜欢、情绪象限等变量,提出了结构化因果模型(SCM)。为了评估这种方法,研究人员从观看LIRIS-ACCEDE数据集视频样本的志愿者身上收集了脑电图数据。为了检验年龄、性别和视频内容对喜欢和情感的因果影响,总共统计评估了48个因果效应。结果表明,该方法可以推广到包含所提SCM变量的任何数据集。此外,如果提供了适当的SCM,所提出的方法可以应用于任何其他类似的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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